Gulbrandsen M.L.,Copenhagen University |
Ball L.B.,Crustal Geophysics and Geochemistry Science Center |
Minsley B.J.,Crustal Geophysics and Geochemistry Science Center |
Hansen T.M.,Copenhagen University
Interpretation | Year: 2017
When a geologist sets up a geologic model, various types of disparate information may be available, such as exposures, boreholes, and (or) geophysical data. In recent years, the amount of geophysical data available has been increasing, a trend that is only expected to continue. It is nontrivial (and often, in practice, impossible) for the geologist to take all the details of the geophysical data into account when setting up a geologic model. We have developed an approach that allows for the objective quantification of information from geophysical data and borehole observations in a way that is easy to integrate in the geologic modeling process. This will allow the geologist to make a geologic interpretation that is consistent with the geophysical information at hand. We have determined that automated interpretation of geologic layer boundaries using information from boreholes and geophysical data alone can provide a good geologic layer model, even before manual interpretation has begun. The workflow is implemented on a set of boreholes and airborne electromagnetic (AEM) data from Morrill, Nebraska. From the borehole logs, information about the depth to the base of aquifer (BOA) is extracted and used together with the AEM data to map a surface that represents this geologic contact. Finally, a comparison between our automated approach and a previous manual mapping of the BOA in the region validates the quality of the proposed method and suggests that this workflow will allow a much faster and objective geologic modeling process that is consistent with the available data. © 2017 Society of Exploration Geophysicists and American Association of Petroleum Geologists.
Friedel M.J.,Crustal Geophysics and Geochemistry Science Center |
Friedel M.J.,University of Colorado at Denver
Applied Soft Computing Journal | Year: 2013
Few studies attempt to model the economic feasibility of mining undiscovered mineral resources given the sparseness of data; and the coupled, nonlinear, spatial, and temporal relationships among variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 203 porphyry copper deposit sites across the world. The sparse data set includes one dependent variable indicating the economic feasibility, and seventy two independent variables from categories describing characteristics of mining method, metallurgy, dimensions, economics, and amount. Analysis of component planes reveals relations and strengths in the underlying SOM multivariate density function which are used to impute missing values. Application of the Davies-Bouldin criteria to k-means clusters of SOM neurons identified 14 regional economic resource units (conceptual models). A best subsets approach applied to median values from these models identified 20 statistically significant combinations of variables. During model fitting by the multiple linear regression technique, only four of the empirical models had variables that were all significant at the 95% confidence level. The best model explained 98% of the variability in economic feasibility and incorporated variables describing distance to natural gas, road, and water; and the total amount of resources. This model was independently validated by comparing predictions of economic feasibility at 68 mine sites not included in the training data. Eighty-four percent of the reported economic feasibility is correctly predicted with 8 false positives and 2 false negative. We demonstrate the application of this model to a permissive copper porphyry tract that crosses a portion of British Columbia and Yukon territories of Canada. The proposed hybrid approach provides an alternative modeling paradigm for translating estimates of contained metal into meaningful societal measures. © Published by Elsevier B.V.
Shelton J.L.,Eastern Energy Resources Science Center |
Shelton J.L.,Colorado School of Mines |
McIntosh J.C.,Eastern Energy Resources Science Center |
McIntosh J.C.,University of Arizona |
And 6 more authors.
International Journal of Greenhouse Gas Control | Year: 2016
Rising atmospheric carbon dioxide (CO2) concentrations are fueling anthropogenic climate change. Geologic sequestration of anthropogenic CO2 in depleted oil reservoirs is one option for reducing CO2 emissions to the atmosphere while enhancing oil recovery. In order to evaluate the feasibility of using enhanced oil recovery (EOR) sites in the United States for permanent CO2 storage, an active multi-stage miscible CO2 flooding project in the Permian Basin (North Ward Estes Field, near Wickett, Texas) was investigated. In addition, two major natural CO2 reservoirs in the southeastern Paradox Basin (McElmo Dome and Doe Canyon) were also investigated as they provide CO2 for EOR operations in the Permian Basin. Produced gas and water were collected from three different CO2 flooding phases (with different start dates) within the North Ward Estes Field to evaluate possible CO2 storage mechanisms and amounts of total CO2 retention. McElmo Dome and Doe Canyon were sampled for produced gas to determine the noble gas and stable isotope signature of the original injected EOR gas and to confirm the source of this naturally-occurring CO2. As expected, the natural CO2 produced from McElmo Dome and Doe Canyon is a mix of mantle and crustal sources. When comparing CO2 injection and production rates for the CO2 floods in the North Ward Estes Field, it appears that CO2 retention in the reservoir decreased over the course of the three injections, retaining 39%, 49% and 61% of the injected CO2 for the 2008, 2010, and 2013 projects, respectively, characteristic of maturing CO2 miscible flood projects. Noble gas isotopic composition of the injected and produced gas for the flood projects suggest no active fractionation, while δ13C[sbnd]CO2 values suggest no active CO2 dissolution into formation water, or mineralization. CO2 volumes capable of dissolving in residual formation fluids were also estimated along with the potential to store pure-phase supercritical CO2. Using a combination of dissolution trapping and residual trapping, both volumes of CO2 currently retained in the 2008 and 2013 projects could be justified, suggesting no major leakage is occurring. These subsurface reservoirs, jointly considered, have the capacity to store up to 9 years of CO2 emissions from an average US powerplant. © 2016
Friedel M.J.,Crustal Geophysics and Geochemistry Science Center
Environmental Modelling and Software | Year: 2011
Few studies attempt to model the range of possible post-fire hydrologic and geomorphic hazards because of the sparseness of data and the coupled, nonlinear, spatial, and temporal relationships among landscape variables. In this study, a type of unsupervised artificial neural network, called a self-organized map (SOM), is trained using data from 540 burned basins in the western United States. The sparsely populated data set includes variables from independent numerical landscape categories (climate, land surface form, geologic texture, and post-fire condition), independent landscape classes (bedrock geology and state), and dependent initiation processes (runoff, landslide, and runoff and landslide combination) and responses (debris flows, floods, and no events). Pattern analysis of the SOM-based component planes is used to identify and interpret relations among the variables. Application of the Davies-Bouldin criteria following k-means clustering of the SOM neurons identified eight conceptual regional models for focusing future research and empirical model development. A split-sample validation on 60 independent basins (not included in the training) indicates that simultaneous predictions of initiation process and response types are at least 78% accurate. As climate shifts from wet to dry conditions, forecasts across the burned landscape reveal a decreasing trend in the total number of debris flow, flood, and runoff events with considerable variability among individual basins. These findings suggest the SOM may be useful in forecasting real-time post-fire hazards, and long-term post-recovery processes and effects of climate change scenarios. © 2011.
Akbari Esfahani A.,University of Colorado at Denver |
Akbari Esfahani A.,Crustal Geophysics and Geochemistry Science Center |
Friedel M.J.,University of Colorado at Denver |
Friedel M.J.,Crustal Geophysics and Geochemistry Science Center
Environmental Modelling and Software | Year: 2014
A novel approach is proposed to forecast the likelihood of climate-change across spatial landscape gradients. This hybrid approach involves reconstructing past precipitation and temperature using the self-organizing map technique; determining quantile trends in the climate-change variables by quantile regression modeling; and computing conditional forecasts of climate-change variables based on self-similarity in quantile trends using the fractionally differenced auto-regressive integrated moving average technique. The proposed modeling approach is applied to states (Arizona, California, Colorado, Nevada, New Mexico, and Utah) in the southwestern U.S., where conditional forecasts of climate-change variables are evaluated against recent (2012) observations, evaluated at a future time period (2030), and evaluated as future trends (2009-2059). These results have broad economic, political, and social implications because they quantify uncertainty in climate-change forecasts affecting various sectors of society. Another benefit of the proposed hybrid approach is that it can be extended to any spatiotemporal scale providing self-similarity exists. © 2013.
Todorov T.I.,Crustal Geophysics and Geochemistry Science Center |
Ejnik J.W.,University of Wisconsin - Whitewater |
Guandalini G.,The Joint Pathology Center |
Xu H.,The Joint Pathology Center |
And 5 more authors.
Journal of Trace Elements in Medicine and Biology | Year: 2013
In this study we report uranium analysis for human semen samples. Uranium quantification was performed by inductively coupled plasma mass spectrometry. No additives, such as chymotrypsin or bovine serum albumin, were used for semen liquefaction, as they showed significant uranium content. For method validation we spiked 2. g aliquots of pooled control semen at three different levels of uranium: low at 5. pg/g, medium at 50. pg/g, and high at 1000. pg/g. The detection limit was determined to be 0.8. pg/g uranium in human semen. The data reproduced within 1.4-7% RSD and spike recoveries were 97-100%. The uranium level of the unspiked, pooled control semen was 2.9. pg/g of semen (. n=. 10). In addition six semen samples from a cohort of Veterans exposed to depleted uranium (DU) in the 1991 Gulf War were analyzed with no knowledge of their exposure history. Uranium levels in the Veterans' semen samples ranged from undetectable (<0.8. pg/g) to 3350. pg/g. This wide concentration range for uranium in semen is consistent with known differences in current DU body burdens in these individuals, some of whom have retained embedded DU fragments. © 2012.
PubMed | Crustal Geophysics and Geochemistry Science Center
Type: Journal Article | Journal: Journal of trace elements in medicine and biology : organ of the Society for Minerals and Trace Elements (GMS) | Year: 2013
In this study we report uranium analysis for human semen samples. Uranium quantification was performed by inductively coupled plasma mass spectrometry. No additives, such as chymotrypsin or bovine serum albumin, were used for semen liquefaction, as they showed significant uranium content. For method validation we spiked 2g aliquots of pooled control semen at three different levels of uranium: low at 5 pg/g, medium at 50 pg/g, and high at 1000 pg/g. The detection limit was determined to be 0.8 pg/g uranium in human semen. The data reproduced within 1.4-7% RSD and spike recoveries were 97-100%. The uranium level of the unspiked, pooled control semen was 2.9 pg/g of semen (n=10). In addition six semen samples from a cohort of Veterans exposed to depleted uranium (DU) in the 1991 Gulf War were analyzed with no knowledge of their exposure history. Uranium levels in the Veterans semen samples ranged from undetectable (<0.8 pg/g) to 3350 pg/g. This wide concentration range for uranium in semen is consistent with known differences in current DU body burdens in these individuals, some of whom have retained embedded DU fragments.